Exploring daily wind data using the Meteostat Python Library
Group Members: Travis, Ira, Micah
Course: Data Science – Phase 1
Goal: Explore and compare wind trends in distinct U.S. regions
– Florida (hurricanes)
– Oklahoma (tornado alley)
– Pennsylvania (home)
– California (coastal)
Source: Meteostat Python API
Dataset Type: Daily aggregated weather observations per station
Key Variableswspd: Average wind speed (km/h)wpgt: Maximum wind gust (km/h)wdir: Mean wind direction (degrees)tavg – Average air temperature (°C)Units: Metric (km/h, hPa, °C)
| Region | Station (City) | Station ID | Climate Context |
|---|---|---|---|
| Florida | Miami Intl Airport | 72202 | Hurricane-prone coastal region |
| Oklahoma | Oklahoma City | 72353 | Tornado Alley with frequent severe winds |
| Pennsylvania | Pittsburgh Intl Airport | 72520 | Inland relative known temperature |
| California | Los Angeles Intl Airport | 72295 | Pacific coastal winds and mountain effects |